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Neville J Ford

Publications and source records attributed to Neville J Ford.

3 recordsLinked to original sources

A genetic-algorithm approach to simulating human immunodeficiency virus evolution reveals the strong impact of multiply infected cells and recombination.

It has been previously shown that the majority of human immunodeficiency virus type 1 (HIV-1)-infected splenocytes can harbour multiple, divergent proviruses with a copy number ranging from one to eight. This implies that, besides point mutations, recombination should be considered as an important mechanism in the evolution of HIV within an infected host. To explore in detail the possible contributions of multi-infection and recombination to HIV evolution, the effects of major microscopic parameters of HIV replication (i.e. the point-mutation rate, the crossover number, the recombination rate and the provirus copy number) on macroscopic characteristics (such as the Hamming distance and the abundance of n-point mutants) have been simulated in silico. Simulations predict that multiple provirus copies per infected cell and recombination act in synergy to speed up the development of sequence diversity. Point mutations can be fixed for some time without fitness selection. The time needed for the selection of multiple mutations with increased fitness is highly variable, supporting the view that stochastic processes may contribute substantially to the kinetics of HIV variation in vivo.

Algorithms↗

A mathematical approach for optimizing dendritic cell-based immunotherapy.

Adoptive dendritic cell (DC)-based immunotherapy represents a promising approach to overcome peripheral tolerance against autologous tumor antigens and to maintain protective antitumor immunity. The translation of successful preclinical studies, however, appears to be hampered by new complexities associated with the clinical situation. Mathematical modeling provides the means for qualitative and quantitative analysis, predictions for complex dynamic systems in immunology, and for the design and improvement of therapeutic approaches. We present here a workable computational methodology for developing meaningful data- and hypothesis-driven mathematical models for DC-based immunotherapy with a particular focus on numerical parameter estimation and sensitivity analysis.

CD8-Positive T-Lymphocytes↗

Determining control parameters for dendritic cell-cytotoxic T lymphocyte interaction.

Dendritic cells (DC) are potent immunostimulatory cells facilitating antigen transport to lymphoid tissues and providing efficient stimulation of T cells. A series of experimental studies in mice demonstrated that cytotoxic T lymphocytes (CTL) can be efficiently induced by adoptive transfer of antigen-presenting DC. However, the success of DC-based immunotherapeutic treatment of human cancer, for example, is still limited because the details of the regulation and kinetics of the DC-CTL interaction are not yet completely understood. Using a combination of experimental mouse studies, mathematical modeling, and nonlinear parameter estimation, we analyzed the population dynamics of DC-induced CTL responses. The model integrates a predator-prey-type interaction of DC and CTL with the non-linear compartmental dynamics of T cells. We found that T cell receptor avidity, the half-life of DC, and the rate of CTL-mediated DC-elimination are the major control parameters for optimal DC-induced CTL responses. For induction of high avidity CTL, the number of adoptively transferred DC was of minor importance once a minimal threshold of approximately 200 cells per spleen had been reached. Taken together, our study indicates that the availability of high avidity T cells in the recipient in combination with the optimal application regimen is of prime importance for successful DC-based immunotherapy.

Animals↗